Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed Text Article Swipe
Tobore Igbe
,
Boris Kovatchev
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/19322968251323913
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/19322968251323913
Background: The emergence of continuous glucose monitoring (CGM) devices has not only revolutionized diabetes management but has also opened new avenues for research. This article presents a novel approach to encoding a CGM daily profile into a CGM string and CGM text that preserves clinical metrics information but compresses the data. Methods: Eight alphabets were defined to represent glucose ranges. The Akaike information criterion (AIC) was derived from error, and the compression ratio was estimated for each alphabet to determine the optimal alphabet for encoding the CGM daily profile. The analysis was done with data from six distinct studies, with different treatment modalities, applied to individuals with type 1 diabetes (T1D) or type 2 diabetes (T2D), and without diabetes. The data set was divided into 70% for training and 30% for validation. Result: The result from the training data reveals that a 9-letter alphabet was optimal for encoding daily CGM profiles for T1D or T2D, yielding the lowest AIC score that minimizes information loss. However, in health, fewer letters were needed, and this is to be expected, given the lower variation of the data. Further testing with the Pearson correlation showed that the 9-letter alphabet approximated the coefficient of variation, with correlations between 0.945 and 0.965. Conclusion: Encoding CGM data into text could enhance the classification of CGM profiles and enable the use of well-established search engines with CGM data. Other potential applications include predictive modeling, anomaly detection, indexing, trend analysis, or future generative artificial intelligence applications for diabetes research and clinical practice.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/19322968251323913
- OA Status
- green
- Cited By
- 1
- References
- 28
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4408675512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/19322968251323913Digital Object Identifier
- Title
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Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed TextWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-20Full publication date if available
- Authors
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Tobore Igbe, Boris KovatchevList of authors in order
- Landing page
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https://doi.org/10.1177/19322968251323913Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11924066Direct OA link when available
- Concepts
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Akaike information criterion, Computer science, Encoding (memory), Alphabet, Pearson product-moment correlation coefficient, Pattern recognition (psychology), Artificial intelligence, Data mining, Mathematics, Statistics, Machine learning, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(AIC) | 64 |
| abstract_inverted_index.(CGM) | 7 |
| abstract_inverted_index.(T1D) | 110 |
| abstract_inverted_index.0.945 | 203 |
| abstract_inverted_index.Eight | 52 |
| abstract_inverted_index.Other | 230 |
| abstract_inverted_index.could | 212 |
| abstract_inverted_index.daily | 33, 87, 148 |
| abstract_inverted_index.data. | 50, 183, 229 |
| abstract_inverted_index.fewer | 167 |
| abstract_inverted_index.given | 177 |
| abstract_inverted_index.loss. | 163 |
| abstract_inverted_index.lower | 179 |
| abstract_inverted_index.novel | 27 |
| abstract_inverted_index.ratio | 72 |
| abstract_inverted_index.score | 159 |
| abstract_inverted_index.trend | 239 |
| abstract_inverted_index.(T2D), | 115 |
| abstract_inverted_index.0.965. | 205 |
| abstract_inverted_index.Akaike | 61 |
| abstract_inverted_index.enable | 220 |
| abstract_inverted_index.error, | 68 |
| abstract_inverted_index.future | 242 |
| abstract_inverted_index.lowest | 157 |
| abstract_inverted_index.opened | 18 |
| abstract_inverted_index.result | 134 |
| abstract_inverted_index.search | 225 |
| abstract_inverted_index.showed | 190 |
| abstract_inverted_index.string | 38 |
| abstract_inverted_index.Further | 184 |
| abstract_inverted_index.Pearson | 188 |
| abstract_inverted_index.Result: | 132 |
| abstract_inverted_index.anomaly | 236 |
| abstract_inverted_index.applied | 103 |
| abstract_inverted_index.article | 24 |
| abstract_inverted_index.avenues | 20 |
| abstract_inverted_index.between | 202 |
| abstract_inverted_index.defined | 55 |
| abstract_inverted_index.derived | 66 |
| abstract_inverted_index.devices | 8 |
| abstract_inverted_index.divided | 123 |
| abstract_inverted_index.engines | 226 |
| abstract_inverted_index.enhance | 213 |
| abstract_inverted_index.glucose | 5, 58 |
| abstract_inverted_index.health, | 166 |
| abstract_inverted_index.include | 233 |
| abstract_inverted_index.letters | 168 |
| abstract_inverted_index.metrics | 45 |
| abstract_inverted_index.needed, | 170 |
| abstract_inverted_index.optimal | 81, 145 |
| abstract_inverted_index.profile | 34 |
| abstract_inverted_index.ranges. | 59 |
| abstract_inverted_index.reveals | 139 |
| abstract_inverted_index.testing | 185 |
| abstract_inverted_index.without | 117 |
| abstract_inverted_index.9-letter | 142, 193 |
| abstract_inverted_index.Encoding | 207 |
| abstract_inverted_index.However, | 164 |
| abstract_inverted_index.Methods: | 51 |
| abstract_inverted_index.alphabet | 77, 82, 143, 194 |
| abstract_inverted_index.analysis | 90 |
| abstract_inverted_index.approach | 28 |
| abstract_inverted_index.clinical | 44, 251 |
| abstract_inverted_index.diabetes | 13, 109, 114, 248 |
| abstract_inverted_index.distinct | 97 |
| abstract_inverted_index.encoding | 30, 84, 147 |
| abstract_inverted_index.presents | 25 |
| abstract_inverted_index.profile. | 88 |
| abstract_inverted_index.profiles | 150, 218 |
| abstract_inverted_index.research | 249 |
| abstract_inverted_index.studies, | 98 |
| abstract_inverted_index.training | 127, 137 |
| abstract_inverted_index.yielding | 155 |
| abstract_inverted_index.alphabets | 53 |
| abstract_inverted_index.analysis, | 240 |
| abstract_inverted_index.criterion | 63 |
| abstract_inverted_index.determine | 79 |
| abstract_inverted_index.diabetes. | 118 |
| abstract_inverted_index.different | 100 |
| abstract_inverted_index.emergence | 2 |
| abstract_inverted_index.estimated | 74 |
| abstract_inverted_index.expected, | 176 |
| abstract_inverted_index.indexing, | 238 |
| abstract_inverted_index.minimizes | 161 |
| abstract_inverted_index.modeling, | 235 |
| abstract_inverted_index.potential | 231 |
| abstract_inverted_index.practice. | 252 |
| abstract_inverted_index.preserves | 43 |
| abstract_inverted_index.represent | 57 |
| abstract_inverted_index.research. | 22 |
| abstract_inverted_index.treatment | 101 |
| abstract_inverted_index.variation | 180 |
| abstract_inverted_index.artificial | 244 |
| abstract_inverted_index.compresses | 48 |
| abstract_inverted_index.continuous | 4 |
| abstract_inverted_index.detection, | 237 |
| abstract_inverted_index.generative | 243 |
| abstract_inverted_index.management | 14 |
| abstract_inverted_index.monitoring | 6 |
| abstract_inverted_index.predictive | 234 |
| abstract_inverted_index.variation, | 199 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Conclusion: | 206 |
| abstract_inverted_index.coefficient | 197 |
| abstract_inverted_index.compression | 71 |
| abstract_inverted_index.correlation | 189 |
| abstract_inverted_index.individuals | 105 |
| abstract_inverted_index.information | 46, 62, 162 |
| abstract_inverted_index.modalities, | 102 |
| abstract_inverted_index.validation. | 131 |
| abstract_inverted_index.applications | 232, 246 |
| abstract_inverted_index.approximated | 195 |
| abstract_inverted_index.correlations | 201 |
| abstract_inverted_index.intelligence | 245 |
| abstract_inverted_index.classification | 215 |
| abstract_inverted_index.revolutionized | 12 |
| abstract_inverted_index.well-established | 224 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.84292353 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |